Early approaches followed the idea of learning semantic classifiers for seen classes and then classifying the visual patterns by predicting semantic descriptions and comparing them with descriptions of unseen classes. In this space, Lampert et al. [1]} propose attribute prediction, using the posterior of each semantic description. The SJE model [2]} uses multiple compatibility functions to construct a joint embedding space. ESZSL [3]} uses a Frobenius norm
regularizer to learn an embedding space. In videos,
there are additional challenges: action labels need more complex representations than objects and hence give rise to more complex manual annotations.
